Course Aims: The aim of this course is to enable students to apply basic statistical inference methods for tackling real-world business questions and equip them with basic knowledge of the Python programming package.

Course Aims: The aim of the course is to give students hands-on experience in using industry-standard simulation modelling software in order to structure and solve complex and large-scale managerial decision problems

Course Aims: This course introduces classical multivariate analysis and techniques which are useful for analyzing both designed experiments and observational studies

Course Aims: This course introduces sophisticated statistical techniques and models for analyzing time series data

Course Aims: This course introduces students the basic concepts of regression analysis.  The course contents will cover the linear regression models about continuous data and generalized linear regression models about categorical data.  The major aim is to equip students the fundamental knowledge to apply the regression models in real data analysis

The aim of this course is to introduce students to optimization modelling and solution techniques, typical applications areas within strategic/operation business planning, and the use of commercial optimization software

This course introduces students systematically to the range of activities involved in Production and Operations Management, mainly adopting quantitative approaches and the main quantitative and other analytical approaches used in Productions and Operations Management (and related areas).

In this course, students will learn about the fundamentals of machine learning and forecasting techniques and gain hands-on experience with analysing and solving a variety of problems encountered in business and management

The course will cover the use of spreadsheets to structure, analyze, and solve complex managerial problems. Students will be introduced to both basic and advanced features of Microsoft Excel, which are highly sought-after skills in the job market

The course will cover performance management, soft systems methodologies, and data envelope analysis (DEA) models

To introduce the foundation concepts of market response models, and discuss techniques and findings spawned by the marketing information revolution. To study a framework for considering the various bases and methods available for conducting segmentation studies, and discuss the methodology for market segmentation from traditional techniques to more recent developments in finite mixtures and latent class models

This course aims to introduce students to the power of big data analytics and data visualisation techniques in contributing to business value creation. The module will also enable students to solve a variety of complex data centred business problems using computer software tools like R and Gephi

This course introduces the overall planning of the survey operation and design and selection of samples and the design of questionnaires; the various survey sampling methods and the corresponding data analysis, especially the estimation methods of population mean and proportion. This course also introduces various kinds of experimental designs involving factorial and uniform designs as well as design for computer experiments

This course introduces the mathematics of risk and insurance, life contingencies as applied to models including expenses, non-forfeiture benefits, dividends, and valuation theory for pension plans

This course introduces the fundamental idea, techniques and algorithms for network, transportation, and assignment models, as well as project management

This course introduces computational methods for problems of finance, including mainly the computation of market indicators and option price

The work-based learning course to give an opportunity for students to apply the skills and knowledge from the M.SC. programme to local companies/industries

This course introduces the fundamental concepts of financial derivatives and portfolio risk measurement and management. Students will learn why both firms and individual investors should learn how to measure and manage risk. To highlight the practical relevance of the course materials we shall discuss a number of real-word case studies throughout the course.

This course introduces advanced theory and algorithms for linear programming, dynamic programming and nonlinear programming. Numerous examples will be adopted to demonstrate the use of various algorithms and techniques involved. The emphasis is not only on mastering these algorithms and techniques but also on the applications of them on various practical problems

This course introduces students the basic concepts of data analytics and equip the skill to build the model to solve the problem theoretically and practically

This course guides students in the development of research methodology appropriate to the practice of Operational Research and Business Statistics, and gives students the opportunity to work on problems of Operational Research and Business Statistics that have real-world significance.

This course guides students in the development of research methodology appropriate to the practice of Operational Research and Business Statistics, and gives students the opportunity to work on problems of Operational Research and Business Statistics that have real-world significance